Toward optical signal processing using Photonic Reservoir Computing

Vandoorne, Kristof; Dierckx, Wouter; Schrauwen, Benjamin; Verstraeten, David; Baets, Roel; Bienstman, Peter; Campenhout, J. Van

doi:10.1364/oe.16.011182

Cited by 224 publications

(126 citation statements)

References 16 publications

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“…Another possible concept that relies merely on activities to represent memory and learning is reservoir computing ( Vandoorne et al 2008;Büsing et al 2010) of which liquid state machines (Maass et al 2002) and echo state networks (Jaeger and Haas 2004) are the most prominent. The abstract idea of this concept is that a complex network of calculating identities (e.g., neurons) is so diverse that each task is solved somewhere within the network (Maass et al 2002;Buonomano and Maass 2009;Maass 2010).…”

Section: Physiological Mechanismmentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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If we stored every bit of input, the storage capacity of our nervous system would be reached after only about 10 days. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis.

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Section: Physiological Mechanismmentioning

confidence: 99%

Time scales of memory, learning, and plasticity

et al. 2012

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“…As such, a silicon photonic RC chip is an attractive system for ultra high speed and low-power consumption optical computing. Already in 2008, Vandoorne et al [35] suggested the implementation of photonic RC in an on-chip network of SOAs. Consequently, the computational performance of SOAs connected in a waterfall topology was evaluated numerically.…”

Section: On Chip Silicon Photonics Reservoir Computermentioning

confidence: 99%

“…This function can be computed efficiently, and as such numerical RC systems are often based on the tanh as the nonlinearity of the nodes. For the first photonic RC, it was therefore intended to optically reproduce the encouraging performance of the numerical counterparts [35][36][37]. It was, however, quickly realized that constantly driving a SOA into power saturation results in poor energy efficiency.…”

Section: On Chip Silicon Photonics Reservoir Computermentioning

confidence: 99%

Advances in photonic reservoir computing

2017

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Abstract:We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir's complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

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“…Finally, RC is being extended to various domains (e.g. robotics [13], photonics [14] and electronics [15]). Given an equivalent to the weight matrix in another domain and an approximation of the maximum gain of the system, one can define an equivalent to the spectral radius.…”

Section: Echo State Property Revisitedmentioning

confidence: 99%

The spectral radius remains a valid indicator of the Echo state property for large reservoirs

Caluwaerts

wyffels

Dieleman

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Abstract-In the field of Reservoir Computing, scaling the spectral radius of the weight matrix of a random recurrent neural network to below unity is a commonly used method to ensure the Echo State Property. Recently it has been shown that this condition is too weak. To overcome this problem, othermore involved -sufficient conditions for the Echo State Property have been proposed. In this paper we provide a large-scale experimental verification of the Echo State Property for large recurrent neural networks with zero input and zero bias. Our main conclusion is that the spectral radius method remains a valid indicator of the Echo State Property; the probability that the Echo State Property does not hold, drops for larger networks with spectral radius below unity, which are the ones of practical interest.

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Toward optical signal processing using Photonic Reservoir Computing

Cited by 224 publications

References 16 publications

Time scales of memory, learning, and plasticity

Time scales of memory, learning, and plasticity

Advances in photonic reservoir computing

The spectral radius remains a valid indicator of the Echo state property for large reservoirs

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